Title: Automated breast cancer segmentation and classification in mammogram images using deep learning approach

Authors: B. Dhanalaxmi; N. Venkatesh; Yeligeti Raju; G. Jagan Naik; Channapragada Rama Seshagiri Rao; V. Prema Tulasi

Addresses: Department CSE-Cyber Security (CS), Geethanjali College of Engineering and Technology, Cheeryal, Keesara, Hyderabad, 9866546280, India ' School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India ' Department of Computer Science and Engineering (Data Science), Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India ' Department of Computer Science and Engineering (Data Science), CMR Institute of Technology, Kandlakoya (V), Medchal Road, Hyderabad, India ' Pallavi Engineering College, Nagole, Hyderabad, Telangana, India ' Department of Computer Science and Engineering (Data Science), CMR Technical Campus (Autonomous), Hyderabad, Telangana, India

Abstract: One of the most prevalent cancers among women is breast cancer. The mortality rate of this cancer may be lowered with an early diagnosis. In the literature, a wide range of AI-based techniques have been proposed. Nevertheless, they face several difficulties, including inadequate training models, irrelevant feature extraction, and similarities between cancerous and non-cancerous regions. Therefore, we propose a novel improved deep learning-based model for the segmentation and classification of breast cancer in this research. An enhanced UNet++ (EUNet++) model is used to segment the affected part of the lesion region. The improved ResNext (IResNext) model classifies mammogram images into benign and malignant classes. The findings showed that the suggested framework outperformed other models trained on the same dataset, achieving an exceptional 99.56% classification accuracy for the CBIS-DDSM dataset and 99.64% for the INbreast dataset.

Keywords: breast cancer; deep learning; enhanced UNet++; improved ResNext; mammogram images; segmentation; classification.

DOI: 10.1504/IJBET.2025.144814

International Journal of Biomedical Engineering and Technology, 2025 Vol.47 No.2, pp.165 - 193

Received: 12 Apr 2024
Accepted: 10 Jul 2024
Published online: 03 Mar 2025 *

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